I am the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. I am associated with the theory group, the ASSET Center on safe, explainable, and trustworthy AI systems, and the Warren Center for network and data sciences.

My research interests lie at the intersection of theoretical computer science and machine learning, with a focus on developing theoretical foundations for modern machine learning paradigms especially deep learning.

Prior to this, I was a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group. I obtained my Ph.D. in the Computer Science department at the University of Texas at Austin advised by Adam Klivans. My dissertation was awarded UTCS’s Bert Kay Dissertation award. My Ph.D. research was generously supported by the JP Morgan AI Fellowship and several fellowships from UT Austin. During my PhD, I visited IAS for the Theoretical Machine learning program and the Simons Institute for the Theory of Computing at UC Berkeley for the Foundations of Deep Learning program (supported by the Simons-Berkeley Research Fellowship). Before that, I received my Bachelors degree from Indian Institute of Technology (IIT) Delhi majoring in Computer Science and Engineering.

For prospective students who are interested in working with me: Please apply to the CIS PhD program and list me as a potential advisor. Unfortunately I will not be able to respond to individual emails from prospective PhD applicants at this time. If you are a current UPenn student looking to do an independent research project, send me an email with your CV, an overview of your research interests, and a brief description of 1-2 recent papers (not mine) you have read and enjoyed. I do not have any current opportunities for external students.

In Fall 2023, I will teach a special topics course CIS 7000: Foundations of Modern Machine Learning: Theory and Empirics. In Spring 2023, I co-taught CIS 5200: Machine Learning with Eric Wong.

Download my resumé.